Continual Test-Time Adaptation (CTTA) is an emerging and challenging task
where a model trained in a source domain must adapt to continuously changing
conditions during testing, without access to the original source data. CTTA is
prone to error accumulation due to uncontrollable domain shifts, leading to
blurred decision boundaries between categories. Existing CTTA methods primarily
focus on suppressing domain shifts, which proves inadequate during the
unsupervised test phase. In contrast, we introduce a novel approach that guides
rather than suppresses these shifts. Specifically, we propose
$\textbf{C}$ontrollable $\textbf{Co}$ntinual $\textbf{T}$est-$\textbf{T}$ime
$\textbf{A}$daptation (C-CoTTA), which explicitly prevents any single category
from encroaching on others, thereby mitigating the mutual influence between
categories caused by uncontrollable shifts. Moreover, our method reduces the
sensitivity of model to domain transformations, thereby minimizing the
magnitude of category shifts. Extensive quantitative experiments demonstrate
the effectiveness of our method, while qualitative analyses, such as t-SNE
plots, confirm the theoretical validity of our approach.

通过引入一种新颖的方法，即 C-CoTTA，它明确防止任何单个类别侵占其他类别，从而减轻不可控转换引起的各类别之间的相互影响，减少模型对领域转换的敏感性，从而降低类别转变的程度。

可控的持续测试时间自适应

Controllable Continual Test-Time Adaptation

Open Domain Generalization (ODG) is a challenging task as it not only deals
with distribution shifts but also category shifts between the source and target
datasets. To handle this task, the model has to learn a generalizable
representation that can be applied to unseen domains while also identify
unknown classes that were not present during training. Previous work has used
multiple source-specific networks, which involve a high computation cost.
Therefore, this paper proposes a method that can handle ODG using only a single
network. The proposed method utilizes a head that is pre-trained by
linear-probing and employs two regularization terms, each targeting the
regularization of feature extractor and the classification head, respectively.
The two regularization terms fully utilize the pre-trained features and
collaborate to modify the head of the model without excessively altering the
feature extractor. This ensures a smoother softmax output and prevents the
model from being biased towards the source domains. The proposed method shows
improved adaptability to unseen domains and increased capability to detect
unseen classes as well. Extensive experiments show that our method achieves
competitive performance in several benchmarks. We also justify our method with
careful analysis of the effect on the logits, features, and the head.

本文针对开放领域泛化问题，通过使用单个网络以及预训练的头部和两个正则化项进行特征提取、分类和模型修改，使模型适应未知领域并提高对未知类别的检测能力，最终在多个基准测试中取得了有竞争力的性能。